Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization
There is a growing need to predict the severity of vitamin D deficiency (VDD) through non-invasive methods due to its significant global health concerns. For vitamin D-level assessments, the 25-hydroxy vitamin D (25-OH-D) blood test is the standard, but it is often not a practical test. This study i...
Saved in:
| Main Authors: | Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/12/2/200 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis
by: Gopi Battineni, et al.
Published: (2024-12-01) -
ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning
by: Raiyan Jahangir, et al.
Published: (2025-04-01) -
Vitamin D deficiency and iron deficiency anemia
by: M Hosseini, et al.
Published: (2016-04-01) -
Association between Vitamin D Deficiency and Disease Severity in Bronchiectasis
by: Shankar G. Koralli, et al.
Published: (2025-01-01) -
VITAMIN D DEFICIENCY
by: E. A. Potrokhova, et al.
Published: (2014-03-01)